The human eye detects visible light in mostly three broad spectral bands, (red, green, and blue). Hyper-spectral sensors can capture images of fresh food items and provide detailed spatial and spectral information from the light they reflect. For example, when sunlight is reflected by a tomato or by a lettuce, the image captured by a hyper-spectral sensor provides the distribution of light intensities at many wavelengths across a two dimensional array of pixels. This information can be used to detect the shape of tomato or the lettuce as well as biological indicators correlated with the absorption and reflection spectrum of certain biological molecules in the tomato or lettuce across many narrow spectral bands measured by a hyperspectral sensor.
Instead of providing only the pixels' intensity variation using the broad color filters red, blue and green from a normal digital camera sensor, a hyper-spectral sensor may provide pixels with intensity variation measured in as many as 128 narrow band spectral filters or more. The number of usable bands is increasing, and is expected to increase in the future. The more bands, the more information that may be extracted. This level of resolution can be used by processing software to detect many characteristics that are not visible with a naked eye or a normal camera, ranging from the water-stress level of the plant to the presence of certain nutrients, fertilizers, insecticides, fungus, and others.